Guavus
概述
总部
美国
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成立年份
2006
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公司类型
私营公司
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收入
$10-100m
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员工人数
51 - 200
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网站
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公司介绍
Guavus 从头开始构建,旨在解决世界上最复杂的数据问题。 Guavus 已在财富 500 强企业中得到验证,它提供了唯一的运营智能平台,可提供跨业务运营的端到端实时视图,目前每天处理超过 5 万亿条记录。 Guavus Reflex® 运营智能平台可实时关联和分析来自多个不同源系统的大量 PB 流式传输和存储的业务、运营和传感器数据。由此产生的“实时”运营分析被输入到新一代分析驱动的应用程序中,以解决网络、服务保障和运营、营销和物联网的特定业务问题。这些解决方案可以嵌入到自动化工作流程和业务流程中,通过将我们的客户转变为实时、数据驱动的企业,使他们变得更加敏捷、更具竞争力和盈利能力。该公司建立在 Guavus Reflex® 平台之上,提供广泛的产品来解决与网络规划、服务保障和运营以及营销和货币化相关的特定业务问题。每个应用程序都包含机器学习和预测分析,使客户能够以以前不可能的方式从他们的数据中快速实现业务价值。业务和上下文感知洞察通过功能强大且易于使用的图形仪表板显示,可以定制以满足特定和个人信息要求。大数据分析解决方案利用基于网格的计算架构,并基于领先的开源技术,包括Hadoop 、Apache Spark 和 Yarn。完整的平台由设置的互连组件组成,包括:收集节点、计算节点、UI 节点和管理节点。
物联网应用简介
技术栈
Guavus的技术栈描绘了Guavus在平台即服务 (paas), 分析与建模, 网络安全和隐私, 和 网络与连接等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
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实例探究.
Case Study
MNO Increases Ad Campaign Conversion Rate for Restaurant’s App by 7X using Guavus-IQ Analytics
A restaurant chain approached a Tier 1 mobile service provider to help launch a campaign sending messages to consumers’ mobile phones, encouraging people to download the restaurant’s free app. The messages were sent to large audience segments, created on the basis of standard demographic information, such as device type and age group of opt-in subscribers. However, the ad campaign was costly, as it was being sent to millions of mobile users, and was yielding poor results – about .6 app downloads per every 100 messages sent. The restaurant chain challenged the mobile operator to improve the conversion rate, agreeing to pay more for each download if the operator could better target their customers.
Case Study
MNO Offers Audience Measurement Service to Advertisers using Guavus-IQ Analytics
The marketing team of a leading Mobile Service Provider had a plan to offer advertising agencies target market insights to optimize their advertising ROI. They planned to gather subscriber analytics including demographic, geographic and behavioral statistics in an anonymous format, and offer this information to current and prospective advertising customers. However, due to the size of the network and volume of data that needed to be analyzed, the option of rolling out a deep packet inspection solution was extremely cost prohibitive and could not be done within the timeframe needed. The complexity of correlating content data with subscriber demographics and geographical location, put the marketing team’s initiative beyond reach.
Case Study
Leading North American MSO Uses Guavus-IQ Analytics to Accelerate Operations and Dramatically Reduce Costs
The corporation, a leading Multiple-System Operator in North America, was facing challenges with delayed problem resolution which was affecting customer satisfaction. The Operations Team was unable to identify the root of the problem and rapidly distinguish between customer premise problems and headend CMTS or video server issues. This led to unnecessary dispatch of technicians to homes, which often turned out to be a headend problem instead of an individual set-top box problem. This resulted in customer frustration and a negative impact on their Net Promoter Score.
Case Study
European MSO Slashes Operational Costs with Guavus-IQ Analytics
A European Multiple-System Operator (MSO) was struggling to rapidly distinguish between issues caused by customer premise devices and headend equipment. This delayed the MSO’s ability to find the root cause of problems and subsequently resolve the issues. With the cost of a truck roll in Germany running about 60 to 70 euros and the handling of incoming customer service calls running about 5 to 10 euros each, the provider hoped to reduce customer service costs and improve customer satisfaction at the same time.
Case Study
MNO Maximizes Campaign Performance for Advertisers using Guavus-IQ Analytics
The marketing team of a Mobile Network Operator (MNO) was struggling to build rich customer profiles to present relevant offers and ads to their subscribers. They needed to match individual preferences and browsing behaviors with subscriber IDs. Additionally, they needed to accurately categorize website URLs viewed with accuracy levels of 80% or greater. However, they were unable to achieve this with the software tools they had in place.
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